Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks
TLDR
In this article , a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture, which helps to make an accurate diagnosis of lung sounds.Abstract:
Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples. International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset was used for lung sound. Here a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture. The combination of pre-processing steps MFCC, Melspectrogram, and Chroma CENS with CNN improvise the performance of the proposed system, which helps to make an accurate diagnosis of lung sounds. The comparative analysis shows how the proposed approach performs better with previous state-of-the-art research approaches. It also shows that there is no need for a wheeze or a crackle to be present in the lung sound to carry out the classification of respiratory pathologies. read more
Citations
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Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network
Hariprasath Manoharan,Radha Krishna Rambola,Pravin R. Kshirsagar,Prasun Chakrabarti,Jarallah Alqahtani,Quadri Noorulhasan Naveed,Saiful Islam,Walelign Dinku Mekuriyaw +7 more
TL;DR: This work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases by using the 120 subjective datasets from public landmarks with and without lung diseases to provide enhanced classification accuracy.
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Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview
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TL;DR: A comprehensive review of prior deep-learning-based architecture lung sound analysis can be found in this article , which discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings.
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ANN-Based Classification of Rain Acoustic Sensor Data Using Modified Mel Frequency Cepstral Coefficients
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ANN-Based Classification of Rain Acoustic Sensor Data Using Modified Mel Frequency Cepstral Coefficients
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Aerial Separation andReceiverArrangements on Identifying Lung Syndromes Using the Artificial Neural Network
Hariprasath Manoharan,Radha Krishna Rambola,Pravin R. Kshirsagar,Prasun Chakrabarti,Jarallah Alqahtani,Quadri Noorulhasan Naveed,Saiful Islam,Walelign Dinku Mekuriyaw +7 more
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